In high-speed optical communications, traditional orbital angular momentum (OAM) multiplexing systems face fundamental limitations, including exponentially increasing spatial-domain complexity, ...

Abstract: Accurate traffic prediction is crucial for the management of intelligent transportation systems. Recently, researchers have developed spatiotemporal graph neural networks (ST-GNNs), ...

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...

Hyperspectral images (HSIs) have very high dimensionality and typically lack sufficient labeled samples, which significantly challenges their processing and analysis. These challenges contribute to ...

Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...

This project implements a Deep Reinforcement Learning (DRL) agent for portfolio management that leverages Wavelet Coherence and Graph Neural Networks (GNNs) to capture dynamic time–frequency ...

Neural networks aren’t the only game in artificial intelligence, but you’d be forgiven for thinking otherwise after the hot streak sparked by ChatGPT’s arrival in 2022. That model’s abilities, ...

Abstract: Exploring the network-level spatiotemporal traffic interplay through traffic pattern recognition or traffic prediction is essential for intelligent traffic systems. Prediction models ...

Introdcution: Parkinson's Disease (PD) is a progressive neurodegenerative disorder that primarily impacts motor function and is prevalent among older adults worldwide. Gait performance (such as speed, ...